Conjugate Gradient (CG)

The Conjugate Gradient (CG) is an algorithm solving linear equation systems in an iterative manner. In this notebook it used to demonstrate de-blurring of an image suffering from optical defocus.

last update: 02/05/2022

Author







Christopher
Hahne, PhD

Data preparation

At the beginning we load an picture showing the Swiss alps which crisp high frequency features. To mimic optical defocus, we convolve the ground-truth image with the Point-Spread Function (PSF), which represents the impulse response of an objective lens and is typically used to model the interferrence of propagating light waves.

from pathlib import Path
import imageio
from scipy.ndimage import convolve
from utils.gibson_lanni import create_psf_kernel

psf_kernel = create_psf_kernel(size=64)[..., -23][16:-16, 16:-16]

path = Path('.') / 'img' / 'alps.png'
gimg = imageio.imread(str(path)).astype('float')

psf_conv = lambda img, kernel=psf_kernel: convolve(img, kernel, mode='reflect', cval=0.0, origin=0)

# blur image
bimg = psf_conv(gimg)

import matplotlib.pyplot as plt
%matplotlib inline
fig, axs = plt.subplots(nrows=1, ncols=3, figsize=(25, 5))
axs[0].imshow(gimg, cmap='gray')
axs[1].imshow(psf_kernel, cmap='gray')
axs[2].imshow(bimg, cmap='gray')
axs[0].set_title('Ground-truth', fontsize=18)
axs[1].set_title('Point-Spread-Function (PSF)', fontsize=18)
axs[2].set_title('Defocused image', fontsize=18)
axs[0].tick_params(top=False, bottom=False, left=False, right=False, labelleft=False, labelbottom=False)
axs[1].tick_params(top=False, bottom=False, left=False, right=False, labelleft=False, labelbottom=False)
axs[2].tick_params(top=False, bottom=False, left=False, right=False, labelleft=False, labelbottom=False)
fig.tight_layout()

Optimization

Our goal in this notebook is to counteract the lens blur and retrieve image information by means of the Conjugate Gradient (CG) algorithm. The objective function in CG writes

$$ \text{arg min}_{\mathbf{x}} \, \frac{1}{2}\mathbf{x}^{\intercal}\mathbf{A}\mathbf{x}−\mathbf{b}^{\intercal}\mathbf{x}+\mathbf{c} $$

where $\mathbf{A}\in\mathbb{R}^{n\times n}$ is symmetric, positive-definite, $\mathbf{b}\in\mathbb{R}^n$ is the observation and $\mathbf{x}\in\mathbb{R}^n$ is the solution. The derivation can be found at the end of this notebook.

Conjugate Gradient Algorithm

For the vanilla version of the CG algorithm, the variable assignment at each update step $k$ shown hereafter. The gain $\alpha_k$ is a scalar given by

$$ \alpha_k = \frac{\mathbf{r}_k^{\intercal} \mathbf{r}_k}{\mathbf{d}_k^{\intercal} \mathbf{A}\mathbf{d}_k} $$

and used in the solution update $\mathbf{x}_{k+1}$, which writes

$$ \mathbf{x}_{k+1} = \mathbf{x}_k + \alpha_k \mathbf{d}_k $$

where $\mathbf{d}_k$ is the direction vector. The residuals $\mathbf{r}_{k}$ are computed via

$$ \mathbf{r}_{k+1} = \begin{cases} \mathbf{r}_k-\alpha_k\mathbf{A}\mathbf{d}_k & \quad \text{if } \mod(k, 10) \neq 0\\ \mathbf{b}-\mathbf{A}\mathbf{x}_k & \quad \text{otherwise} \end{cases} $$

which allows for actual residual inference every now and then to mitigate round-off erros by using the condition from $k$. The CG algorithm comes with another gain denoted by $\beta_k$

$$ \beta_{k+1} = \frac{\mathbf{r}_{k+1}^{\intercal} \mathbf{r}_{k+1}}{\mathbf{r}_{k}^{\intercal} \mathbf{r}_{k}} $$

which helps obtain the direction vector $\mathbf{d}_k$ by

$$ \mathbf{d}_{k+1} = \mathbf{r}_{k+1} \beta_k \mathbf{d}_k $$

using the residuals. An implementation of this procedure is provided below.

def conjugate_gradient(A: callable, b, x = None, rtol: float = 1e-1, max_iter:int=50):
    """
    Conjugate Gradient implementation based on Numpy arrays.

    :param A: function that computes estimate
    :param b: observation
    :param x: initial guess
    :return: x_list
    """

    x = b.copy() if x is None else x
    d = r = b - A(x)
    x_list = [(x.copy()-x.min())/(x.max()-x.min())]

    while len(x_list) < max_iter:

        alpha = (r.ravel().T @ r.ravel()) / (d.ravel().T @ A(d).ravel())
        if alpha < 0: print(f"matrix is not symmetric, positive-definite such that convergence may fail")
        x = x + alpha * d
        if len(x_list) % 10 == 0:
            # compute exact residual to mitigate round-off errors
            r_new = b - A(x)
            d = r
        else:
            r_new = r - alpha * A(d)
        beta = (r_new.ravel().T @ r_new.ravel()) / (r.ravel().T @ r.ravel())
        d = r_new + beta * d
        r = r_new
        
        x_list.append((x.copy()-x.min())/(x.max()-x.min()))

        # see if residual norm below tolerance
        r_norm = (r.flatten() @ r.flatten())**.5
        if r_norm < rtol:
            break

    return x_list

x_cg_list = conjugate_gradient(psf_conv, b=bimg.copy(), max_iter=14)

Results

fig, axs = plt.subplots(nrows=1, ncols=3, figsize=(25, 5))
axs[0].imshow(bimg, cmap='gray')
axs[1].imshow(x_cg_list[-1], cmap='gray')
axs[2].imshow(gimg, cmap='gray')
axs[0].set_title('Defocused image', fontsize=18)
axs[1].set_title('De-blurred image', fontsize=18)
axs[2].set_title('Ground-truth', fontsize=18)
axs[0].tick_params(top=False, bottom=False, left=False, right=False, labelleft=False, labelbottom=False)
axs[1].tick_params(top=False, bottom=False, left=False, right=False, labelleft=False, labelbottom=False)
axs[2].tick_params(top=False, bottom=False, left=False, right=False, labelleft=False, labelbottom=False)
fig.tight_layout()

r_norm = lambda r: (r.flatten() @ r.flatten())**.5
eps_cg_list = [r_norm(bimg - psf_conv(x_cg_list[i])) for i in range(len(x_cg_list))]
print('CGA yields an error of %s after %s iterations.' % (round(eps_cg_list[-1], 9), len(x_cg_list)))

from matplotlib import rc
rc('text', usetex=True)
#rc('font', **{'family' : "sans-serif"})
params= {'text.latex.preamble': r'\usepackage{amsmath}'}
plt.rcParams.update(params)
fig, ax = plt.subplots(figsize=(25, 5))
ax.semilogy(eps_cg_list[1:])
ax.set_ylabel(r'$\lVert\mathbf{r}\rVert_2$', fontsize=18)
ax.set_xlabel(r'Iteration $k$', fontsize=18)
plt.show()

CGA yields an error of 860547.338715539 after 14 iterations.

Animation

imageio.mimwrite(path.parent / 'cg-fit_anim.gif', [(255*img[::3, ::3]).astype('uint8') for img in x_cg_list], format= '.gif', fps = 4)

def cg_iterations_anim(x_list, save_opt=False, style_opt=False):

    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(25, 8))
    canvas = ax.imshow(x_list[0], cmap='gray', vmin=0, vmax=1)
    #txt = ax.text(3, 7, r'Iteration # %s' % str(0), fontsize=18)
    fig.tight_layout()

    def update(i):

        canvas.set_data(x_list[i])
        #txt.set_text(r'Iteration # %s' % str(i))

        return canvas,
    
    import matplotlib as mpl
    if style_opt:
        mpl.rcParams['savefig.facecolor'] = '#148ec8'
        fig.set_facecolor('#148ec8')
        for ax in axs:
            ax.set_facecolor('#148ec8')
            ax.set_title(label=ax.get_title(), fontdict={'color': 'white', 'size': 24}, y=1.0)
            ax.spines['bottom'].set_color('white')
            ax.spines['top'].set_color('white')
            ax.spines['left'].set_color('white')
            ax.spines['right'].set_color('white')
            ax.xaxis.label.set_color('white')
            ax.yaxis.label.set_color('white')
            ax.tick_params(colors='white')
            try:
                ax.zaxis.label.set_color('white')
                ax.w_xaxis.line.set_color("white")
                ax.w_yaxis.line.set_color("white")
                ax.w_zaxis.line.set_color("white")
            except:
                pass
    else:
        mpl.rcParams['savefig.facecolor'] = '#ffffff'
        fig.set_facecolor('#ffffff')
        for ax in axs:
            ax.set_title(label=ax.get_title(), fontdict={'color': 'black', 'size': 24}, y=1.0)
    
    from matplotlib import animation
    anim = animation.FuncAnimation(fig, update, frames=len(x_list), interval=200)
    plt.tight_layout()
    plt.close()
            
    if save_opt:
        anim.save('../img/cg-fit_anim.gif', writer='imagemagick')

    return anim

from IPython.display import HTML
anim = cg_iterations_anim(x_cg_list, save_opt=False)
HTML(anim.to_jshtml())

Once Loop Reflect

Derivation

$$ f(\mathbf{x}) = \frac{1}{2}\mathbf{x}^{\intercal}\mathbf{A}\mathbf{x}−\mathbf{b}^{\intercal}\mathbf{x}+\mathbf{c} $$$$ \frac{\partial f(\mathbf{x})}{\partial \mathbf{x}} = \frac{1}{2}\left(\mathbf{A}^{\intercal}+\mathbf{A}\right)\mathbf{x}−\mathbf{b} $$

which is compressed to

$$ \frac{\partial f(\mathbf{x})}{\partial \mathbf{x}} = \mathbf{A}\mathbf{x}−\mathbf{b} $$

as $\mathbf{A}$ is symmetric such that $\mathbf{A}^{\intercal}+\mathbf{A} = 2\mathbf{A}$. If we set the gradient to zero, we obtain

$$ \mathbf{b} = \mathbf{A}\mathbf{x} \quad\quad \text{if} \quad \frac{\partial f(\mathbf{x})}{\partial \mathbf{x}}=\mathbf{0} $$

such that we write the initial residuals $\mathbf{r}_0$ as

$$ \mathbf{r}_0 = \mathbf{b} - \mathbf{A}\mathbf{x}_0 $$